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Articles 1621 - 1650 of 63010

Full-Text Articles in Computer Sciences

Assessing Urban Flooding And Vegetation Impact In Dubai Creek Following The April 2024 Extreme Rainfall, Dana Alhammadi, Xin Hong Jan 2026

Assessing Urban Flooding And Vegetation Impact In Dubai Creek Following The April 2024 Extreme Rainfall, Dana Alhammadi, Xin Hong

All Works

During April 2024, the United Arab Emirates experienced an unusual phenomenon of an intense rainfall episode between April 14 and 18 that resulted in massive flooding in urban environments, particularly low-lying areas such as Dubai Creek. As a tidal waterway with dense urban development and environmentally sensitive zones surrounding it, Dubai Creek is an ideal site for assessing environmental changes caused by to floods. The study employed pre-flood (14 April) and post-flood (18 April) high-resolution PlanetScope satellite images, in combination with QGIS analysis, to evaluate vegetation health and surface water changes. Quantification of affected areas from flooding was achieved through …

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Mapping Urban Vegetation Changes Using Planetscope Imagery And Gis: A Case Study Of The 2024 Dubai Flood, Sumayya Almansoori, Xin Hong Jan 2026

Mapping Urban Vegetation Changes Using Planetscope Imagery And Gis: A Case Study Of The 2024 Dubai Flood, Sumayya Almansoori, Xin Hong

All Works

In April 2024, unexpected heavy rainfall triggered one of the most severe flooding events in Dubai’s recent history, causing widespread concern for urban infrastructure and green spaces. This study evaluates the flood’s impact on urban vegetation in South Dubai using high-resolution PlanetScope satellite imagery and the Normalized Difference Vegetation Index (NDVI). Vegetation conditions before and after the flood (April 14 and April 18–19, 2024) were quantified and compared using NDVI analysis within QGIS to assess changes in vegetation health and coverage. Results indicate significant declines in vegetation health in areas dominated by intensive turf management, such as Damac Hills and …

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Generative Ai Use And Self-Learning In Higher Education: The Role Of Learning Difficulties, Dana Saleh, Areej Elsayary Jan 2026

Generative Ai Use And Self-Learning In Higher Education: The Role Of Learning Difficulties, Dana Saleh, Areej Elsayary

All Works

The rapid development of GenAI tools and their adoption in education have shown promising potential to personalize learning experiences. However, their effectiveness is influenced by factors such as familiarity, frequency of use, and the impact on self-learning. This study investigates the undergraduate students' familiarity with Generative AI (GenAI) tools, their frequency of use, and the perceived impact of GenAI on self-learning, with particular consideration of differences between students with and without learning difficulties. Prompt engineering is also included as a secondary aspect of students' GenAI experience. The research employed a quantitative survey design, utilizing validated scales to measure familiarity, usage …

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Integrating Adversarial Scenarios Into Llm Security Labs: An Experience Report On A Hands-On Approach, Dominic A. Wilson Jan 2026

Integrating Adversarial Scenarios Into Llm Security Labs: An Experience Report On A Hands-On Approach, Dominic A. Wilson

Journal of Cybersecurity Education, Research and Practice

This paper presents an exploratory case study detailed as a pedagogical experience report on integrating adversarial Large Language Model (LLM) scenarios into a graduate cybersecurity curriculum. In addition to prompt injection, sophisticated techniques such as jailbreaking and model inversion pose emerging threats that traditional computer security curricula often lack. We present the design and implementation of a structured, hands-on module addressing this gap, utilizing a custom Retrieval-Augmented Generation (RAG) platform with local open-source LLMs. A cohort of 16 graduate students participated in this two-week pilot module, engaging in "red team" activities to actively exploit model alignment and privacy vulnerabilities. The …

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Agentic Intelligence Under Constraint: Energy, Context, And The Expansion Of Exchange, Nick Loghmani Jan 2026

Agentic Intelligence Under Constraint: Energy, Context, And The Expansion Of Exchange, Nick Loghmani

iSchool - All Scholarship

Recent advances in agentic artificial intelligence have been driven primarily by scale: larger models, increased data, and expanding computational resources. However, rising energy costs, inference latency, and hardware constraints increasingly challenge this trajectory. This paper argues that intelligence—biological or artificial—does not primarily scale through raw computational expansion, but through the management of exchange under constraint. Drawing on cognitive science, systems theory, and prior work on exchange-based models of intelligence, the paper proposes a theoretical framework in which agentic intelligence scales through context management, proceduralization, and the assembly of reusable units of exchange. Unlike approaches that focus solely on model compression …

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Ai-Driven Automatic Fault Detection Systems: Revolutionizing Modern Smart Grids, Aravind Sanikommu Jan 2026

Ai-Driven Automatic Fault Detection Systems: Revolutionizing Modern Smart Grids, Aravind Sanikommu

Student Theses and Dissertations

The increasing complexity of current power systems, resulting from the integration of distributed generators and renewable energy sources, necessitates intelligent and adaptive fault detection schemes. Traditional protection using impedance and phasor analysis is usually weak when operating in nonlinear and transient operating conditions. Consequently, the tools of Data-driven fault classification and decision-making have gained strength under artificial intelligence (AI) and machine learning (ML) to improve grid reliability. This thesis is a proposal of an automatic fault detection and classification system based on AI applied to a smart mini-grid setting built in MATLAB/Simulink. A complete set of voltage and current data …

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Developing A Multimodal Approach To Channel Characterization On Youtube, Shadi Shajari Jan 2026

Developing A Multimodal Approach To Channel Characterization On Youtube, Shadi Shajari

Theses and Dissertations

YouTube has become a dominant arena for global information sharing, where creators and audiences interact through content, comments, and engagement dynamics. Understanding how these interactions shape a channel’s identity requires a comprehensive characterization of both audience behavior and content structure. This dissertation develops a unified framework for characterizing YouTube channels through multimodal analysis that integrates behavioral modeling, dimensionality reduction, clustering, and content-based characterization to provide a holistic view of audience and editorial patterns. The framework begins by analyzing the structural relationships within co-commenter networks, where users who repeatedly comment on the same videos are connected to capture patterns of interaction …

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Identifying And Overcoming Some Operational Limitations Of Reconfigurable Intelligent Surfaces In 5g And Beyond Wireless Networks, Souvik Deb Jan 2026

Identifying And Overcoming Some Operational Limitations Of Reconfigurable Intelligent Surfaces In 5g And Beyond Wireless Networks, Souvik Deb

Doctoral Theses

Reconfigurable intelligent surfaces (RIS) can dynamically reshape the propagation environment to enhance signal strength, spectral efficiency and reliability in 5th generation (5G) cellular as well as device to device (D2D) communications. However, to reap such benefits, a range of practical and operational challenges need to be addressed for effectively utilizing RIS in realistic urban environments. This includes maintaining line of sight (LoS) between the RIS and the communicating devices for reliable signal reflection in millimeter wave (mmWave) communication, reducing high channel estimation overhead for communication using multipath rich channels and preventing violation of strict latency constraints due to high complexity …

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Oer Review For Open Programming: Java I - Creating An Oer Textbook For Programming Fundamentals, Peter Arsenault Jan 2026

Oer Review For Open Programming: Java I - Creating An Oer Textbook For Programming Fundamentals, Peter Arsenault

Open Educational Resources Publications

This report describes the creation and implementation of a seven‑chapter Open Educational Resource (OER) for Bentley University’s CS 180 – Programming Fundamentals course, developed from the author’s teaching notes, custom examples, and course materials from Fall 2024. The project aimed to provide current, accessible, digital‑first learning resources aligned with modern programming tools, supported by generative‑AI editing in NotebookLM and open‑source formatting tools such as pandoc and Marp. Implemented during Fall 2025, the OER received highly positive student feedback, particularly regarding its clarity, accessibility, and cost savings, and it is slated for further refinement—including updates for Java 25, expanded examples, and …

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Significance, Challenges, And Policy Recommendations For Strengthening Database Development To Support Ai For Science In China, Kaihua Chen, Hongxin Liu, Rui Guo Jan 2026

Significance, Challenges, And Policy Recommendations For Strengthening Database Development To Support Ai For Science In China, Kaihua Chen, Hongxin Liu, Rui Guo

Bulletin of Chinese Academy of Sciences (Chinese Version)

With the rapid emergence of research intelligence driven by big data and artificial intelligence (AI), high-quality, openly shared scientific databases have become a strategic focal point for scientific innovation and enhancing technological competitiveness. Major countries around the world are increasingly recognizing the foundational role of scientific databases in advancing basic research. While continuously strengthening their own scientific data infrastructure through a series of initiatives, they have simultaneously imposed restrictions and suppression on the development of AI technologies in China, including those involving research data. Against this backdrop, building an autonomous and controllable scientific data ecosystem to support research intelligence is …

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Data Altruism: Eu Solution And Path Of Localization In China, Teng Wu Jan 2026

Data Altruism: Eu Solution And Path Of Localization In China, Teng Wu

Bulletin of Chinese Academy of Sciences (Chinese Version)

Data altruism transcends the profit-seeking, monopolistic and competitive nature of the market mechanism. It is driven by innovation and encourages data subjects and holders to share information guided by the public interest. As a new type of data application and service model, it aims to achieve a virtuous cycle of the data ecosystem while optimize the utilization of data resources. Tracing back to the source, the theoretical foundation of data altruism from the ethical theory of altruism, and the concept of its budding was supported by the data for good. Analysis shows that the specific scheme of the EU data …

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Evaluating Corruption Defenses On Learning Adversarial Robustness, Roland Yang, Akhil Kanthamneni Jan 2026

Evaluating Corruption Defenses On Learning Adversarial Robustness, Roland Yang, Akhil Kanthamneni

Journal of the South Carolina Academy of Science

No abstract provided.

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3d Object Tracking Registration Based On Improved Rbot Method, Jiarui Zhou, Haihua Cui, Pengcheng Li, Shihao Gu, Huipu Hao, Xifu Zhao, Anan Zhao, Tao Jiang Jan 2026

3d Object Tracking Registration Based On Improved Rbot Method, Jiarui Zhou, Haihua Cui, Pengcheng Li, Shihao Gu, Huipu Hao, Xifu Zhao, Anan Zhao, Tao Jiang

Journal of System Simulation

Abstract: To address the limitations of region-based object tracking (RBOT) in handling isotropic objects and scenarios with similar foreground-background colors, an improved method integrating edge features is proposed. The approach employs edge detection to extract object contours and designs a region segmentation strategy incorporated into an energy function framework to optimize internal line and edge consistency, thereby enhancing adaptability in dynamic environments and improving pose estimation accuracy. Validation through augmented reality assembly experiments on an aero-engine demonstrates that the proposed method effectively reduces rotational and translational errors, achieving initialization deviations of less than 1.5° and 0.5%, respectively. For static …

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3d Reconstruction For Stadium Cad Drawings Based On Graphic Element Arrangement Pattern Analysis, Shang Ma, Mengyu Zhang, Lan Zhang, Gang Yang Jan 2026

3d Reconstruction For Stadium Cad Drawings Based On Graphic Element Arrangement Pattern Analysis, Shang Ma, Mengyu Zhang, Lan Zhang, Gang Yang

Journal of System Simulation

Abstract: To address the issue of the time-consuming and labor-intensive manual conversion of two-dimensional CAD design drawings of buildings into three-dimensional models, and leveraging the characteristic that stadiums contain a large number of repetitively and regularly arranged objects, this study proposes a similar graphical element detection algorithm. This algorithm detects similarities between graphical elements by constructing their bounding boxes and calculating the L2-Norm distance, identifying all graphical elements of the same type within the CAD drawing. Furthermore, a transformation sequence detection algorithm is proposed. Based on the geometric transformation relationships between graphical elements, a geometric transformation space is defined. By …

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Visual Relocalization Method Combining Region Classification And Local Feature Enhancement, Yining Wang, Yanli Liu, Guanyu Xing Jan 2026

Visual Relocalization Method Combining Region Classification And Local Feature Enhancement, Yining Wang, Yanli Liu, Guanyu Xing

Journal of System Simulation

Abstract: Visual relocalization tasks have important application value in fields such as digital twin and augmented reality. The current mainstream methods still face challenges such as mismatch between coordinate regression scale and receptive field and insufficient attention to local information. A visual relocalization method that combines region classification and local feature enhancement is proposed. The coordinate regression problem in large space is transformed into a multi-region classification problem and a coordinate regression problem inside a small scene, which significantly reduces the uncertainty of coordinate regression and makes the network globally have a large receptive field. A conditioning layer using deep …

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Diffusion Model For Human Motion Generation With Fine-Grained Text And Spatial Control Signals, Binze Jiang, Wenfeng Song, Xia Hou, Shuai Li Jan 2026

Diffusion Model For Human Motion Generation With Fine-Grained Text And Spatial Control Signals, Binze Jiang, Wenfeng Song, Xia Hou, Shuai Li

Journal of System Simulation

Abstract: To improve the accuracy, controllability, and realism of text-driven human motion generation, a novel method is proposed that integrates fine-grained textual semantics with spatial control signals. Within the diffusion model framework, both global text tokens and body-part-level local tokens are introduced. These are encoded using CLIP to obtain corresponding features, which are then fed into the motion diffusion model to enable fine control over different body parts. Spatial guidance is used to dynamically adjust joint positions during the diffusion denoising process, ensuring that the generated motion adheres to spatial constraints. Realism guidance is incorporated to enhance the naturalness and …

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Virtual Reality Rehabilitation Training System Based On Multimodal Brain-Computer Interface, Jing Qu, Kaining Fang, Shantong Zhu, Lingguo Bu Jan 2026

Virtual Reality Rehabilitation Training System Based On Multimodal Brain-Computer Interface, Jing Qu, Kaining Fang, Shantong Zhu, Lingguo Bu

Journal of System Simulation

Abstract: The aging population has led to an increasing demand for rehabilitation for cognitive and motor functions. In response to the lack of interest in traditional rehabilitation and the absence of objective physiological assessment in existing virtual reality (VR) rehabilitation systems, a VR rehabilitation training system based on multimodal brain computer interface is developed by integrating VR interaction, near-infrared brain functional imaging, and motion capture technology. An immersive cognitive-motor integrated training environment was constructed to guide users in completing upper limb tasks. By recruiting subjects and synchronously collecting brain network data and Kinect upper limb motion parameters, multimodal assessment …

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Defect Detection Method Based On Hierarchical Microscopic Feature Modeling And Simulation, Jing Zou, Xu Tan, Junji Mao, Haidong Gao, Jianrong Tan Jan 2026

Defect Detection Method Based On Hierarchical Microscopic Feature Modeling And Simulation, Jing Zou, Xu Tan, Junji Mao, Haidong Gao, Jianrong Tan

Journal of System Simulation

Abstract: To address the challenge of detecting small and low-contrast defects in complex microscopic images, a defect method technology based on hierarchical microscopic feature modeling and simulation is proposed. The method is built on the RT-DETR (real-time detection transformer)framework to construct the HM-RTDETR (hierarchical microscopic RT-DETR) model. It maintains the global feature modeling ability of the Transformer and introduces a Dense O2O-Mosaic, a high-density one-to-one Mosaic augmentation strategy, to increase supervision density for small samples. A depthwise separable convolution (DWConv) module is used to enhance local detail extraction in microscopic textures, and a learnable PatchExpand module is applied …

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Material Reconstruction From Single Image Combining Neural Networks With Singular Value Decomposition, Zhiqiang Li, Xukun Shen, Yong Hu, Xueyang Zhou, Yifan Chen Jan 2026

Material Reconstruction From Single Image Combining Neural Networks With Singular Value Decomposition, Zhiqiang Li, Xukun Shen, Yong Hu, Xueyang Zhou, Yifan Chen

Journal of System Simulation

Abstract: The tabulated BRDFs (bidirectional reflectance distribution function) can realistically reproduce the surface appearance of objects. However, due to their high-dimensional characteristics and the fact that a single planar image contains limited reflectance information and small differences, methods for estimating tabulated BRDFs typically require complex equipment or the capture of multiple images. To address this issue, a method is proposed for reconstructing material properties from a single image by combining neural networks with singular value decomposition. The singular value decomposition is introduced to compress the material into a lower-dimensional space. The task of solving the tabulated BRDFs is simplified to …

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Full-Body Co-Speech Gesture Generation Based On Spatial-Temporal Enhanced Generation Model, Shuozhe Zhang, Wenfeng Song, Xia Hou, Shuai Li Jan 2026

Full-Body Co-Speech Gesture Generation Based On Spatial-Temporal Enhanced Generation Model, Shuozhe Zhang, Wenfeng Song, Xia Hou, Shuai Li

Journal of System Simulation

Abstract: Full-body co-speech gesture generation significantly enhances the interactivity of virtual digital humans, requiring generated gestures to not only align accurately with speech but also demonstrate realistic full-body dynamics. To address limitations of existing methods—Transformer-based approaches often overlook temporal features of action sequences, while diffusion model-based ones inadequately capture spatial correlations between body parts, a full-body action generation method integrating diffusion models, Mamba, and attention mechanisms is proposed. We introduce the spatial self-attention-temporal state space model (STMamba Layer) as the core of denoising network to extract
inter-part spatial features and intra-part temporal features, thus enhancing action quality and diversity. …

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Vrbt: Vr Badminton Training With Multitask Injury Alerts Based On Lightweight 3d Skeletal Reconstruction, Yuning Zhu, Meng Yang, Tianyue Chen, Weiliang Meng Jan 2026

Vrbt: Vr Badminton Training With Multitask Injury Alerts Based On Lightweight 3d Skeletal Reconstruction, Yuning Zhu, Meng Yang, Tianyue Chen, Weiliang Meng

Journal of System Simulation

Abstract: To overcome the limitations of traditional badminton training, a VR training method that integrates multiple models for collaborative simulation is proposed. A "perception-decision- interaction" framework is developed within Unity, featuring diverse training modules powered by a physics engine for realistic trajectory simulation. The system employs a lightweight MHFormer for 3D pose estimation and a novel multi-task model (enhanced injury prediction system, EIPS) that combines random forest and XGBoost to jointly assess injury risk. This approach offers a solution for balancing real-time performance with accuracy in skeleton reconstruction and enables personalized training through dynamic risk assessment.

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Addressing The Void Of Ai Policies In Education For Students With Specific Learning Disabilities, Mikyung Shin, Fatmana Deniz, Latesha Watson, Cynthia Dieterich, Kathy B. Ewoldt, Friggita Johnson, Jennifer E. Kong, Sung Hee Lee, April Whitehurst Jan 2026

Addressing The Void Of Ai Policies In Education For Students With Specific Learning Disabilities, Mikyung Shin, Fatmana Deniz, Latesha Watson, Cynthia Dieterich, Kathy B. Ewoldt, Friggita Johnson, Jennifer E. Kong, Sung Hee Lee, April Whitehurst

Education Faculty Articles and Research

The purpose of this study was to identify the current state of artificial intelligence (AI) policies in U.S. education and propose actionable recommendations through large language model–based topic modeling and Delphi surveys. Out of 12 policy documents released between 2015 and 2025, only two documents (National Center for Learning Disabilities, 2024; W.A. v. Clarksville/Montgomery County School System, 2024) specifically addressed learning disabilities. Policy documents addressing topics such as AI-driven risk assessment, data protection, legal risk management, and ethical guidelines covering other disabilities and general AI in education policy were provided as baselines that could be discussed and validated through …

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Pdr-Stgcn: An Enhanced Stgcn With Multi-Scale Periodic Fusion And A Dynamic Relational Graph For Traffic Forecasting, Jie Hu, Bingbing Tang, Langsha Zhu, Yiting Li, Jianjun Hu, Guanci Yang Jan 2026

Pdr-Stgcn: An Enhanced Stgcn With Multi-Scale Periodic Fusion And A Dynamic Relational Graph For Traffic Forecasting, Jie Hu, Bingbing Tang, Langsha Zhu, Yiting Li, Jianjun Hu, Guanci Yang

Faculty Publications

Accurate traffic flow prediction is a core component of intelligent transportation systems, supporting proactive traffic management, resource optimization, and sustainable urban mobility. However, urban traffic networks exhibit heterogeneous multi-scale periodic patterns and time-varying spatial interactions among road segments, which are not sufficiently captured by many existing spatio-temporal forecasting models. To address this limitation, this paper proposes PDR-STGCN (Periodicity-Aware Dynamic Relational Spatio-Temporal Graph Convolutional Network), an enhanced STGCN framework that jointly models multi-scale periodicity and dynamically evolving spatial dependencies for traffic flow prediction. Specifically, a periodicity-aware embedding module is designed to capture heterogeneous temporal cycles (e.g., daily and weekly patterns) and …

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Military Metaverse: Conceptual Connotation, Construction And Application Framework, Key Issues, Dayong Liu, Zhiming Dong, Jiancheng Gao Jan 2026

Military Metaverse: Conceptual Connotation, Construction And Application Framework, Key Issues, Dayong Liu, Zhiming Dong, Jiancheng Gao

Journal of System Simulation

Abstract: Based on the analysis of the concept of the metaverse, the military metaverse concept model is established and compared with virtual-real fusion systems such as the digital twin battlefield, analyzing its core characteristics and construction significance. To accelerate the construction of the military metaverse, an overall logical architecture for the construction and application of the military metaverse is designed, the concept of military metaverse primitives is proposed, and the technical architecture is designed. The main application directions of the military metaverse are analyzed, and the construction stage division and overall thinking are provided. The key issues in construction and …

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Virtual-Real Fusion Simulation Technology And Application Research For Industrial Control Systems Cybersecurity Of Process Manufacturing, Xinwei Wang, Jinjiang Wang, Zheng Wang, Laibin Zhang Jan 2026

Virtual-Real Fusion Simulation Technology And Application Research For Industrial Control Systems Cybersecurity Of Process Manufacturing, Xinwei Wang, Jinjiang Wang, Zheng Wang, Laibin Zhang

Journal of System Simulation

Abstract: Aiming at the problem that the industrial control system in the process manufacturing industry lacks an effective attack and defense drill platform when facing network attacks, it is difficult to truly simulate the attack situation, verify the protective measures, and accurately evaluate the impact of attacks on the physical system, an industrial control cybersecurity simulation technology based on virtual-real fusion is proposed to build an efficient attack and defense drill range. The industrial control cybersecurity simulation architecture based on virtual-real fusion is designed, and the consistency analysis of virtual-real fusion data is carried out. At the same time, …

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Spatio-Temporal Swin Transformer-Based Flow-Solid Coupling Interaction Sequence Image Prediction Network, Changjun Zou, Zhiyu Ge, Chenxi Zhong Jan 2026

Spatio-Temporal Swin Transformer-Based Flow-Solid Coupling Interaction Sequence Image Prediction Network, Changjun Zou, Zhiyu Ge, Chenxi Zhong

Journal of System Simulation

Abstract: To address limitations in modeling long-term dependencies and multi-scale features in fluidstructure interaction scenarios, a spatiotemporal deep learning model (SwinLSTM) integrating ConvLSTM and Swin Transformer is proposed. The model employs a gated spatiotemporal attention mechanism that dynamically embeds Swin Transformer's window-based multi-head self-attention into ConvLSTM's output gate, enabling adaptive temporal-spatial feature coupling, and designs a multi-level ConvLSTM framework to hierarchically capture complex spatiotemporal correlations. Experiments on a self-built fluid-interaction dataset show that our method achieves the highest PSNR and leading SSIM scores, with superior performance in preserving vortex details and boundary consistency. This work provides an efficient solution …

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Pl-Mamba: A 3d Point Cloud Semantic Segmentation Network Based On Bimodal Fusion, He Zhu, Feng Zhou, Mengxiao Zhu, Ju Dai Jan 2026

Pl-Mamba: A 3d Point Cloud Semantic Segmentation Network Based On Bimodal Fusion, He Zhu, Feng Zhou, Mengxiao Zhu, Ju Dai

Journal of System Simulation

Abstract: To enhance the semantic discrimination capability in point cloud semantic segmentation, a 3D point cloud semantic segmentation network named PL-Mamba is proposed, which is centered on the fusion of point cloud (P) and language (L) dual modalities. This method takes PointMamba as the backbone network, leveraging its excellent long-sequence modeling and global perception capabilities. It introduces a language prompt mechanism and uses a pretrained language model BERT to encode the context of category labels, obtaining semantically rich text features. The text information serves as a language guided token and is deeply integrated with point cloud features through cross modal …

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Dehpr: A Diffusion-Based End-To-End Hand Pose Reconstruction Network, Guoqiong Liao, Longjie Huang, Qingxin Li, Jiajun Zhang, Kefan Chen Jan 2026

Dehpr: A Diffusion-Based End-To-End Hand Pose Reconstruction Network, Guoqiong Liao, Longjie Huang, Qingxin Li, Jiajun Zhang, Kefan Chen

Journal of System Simulation

Abstract: Traditional methods such as convolutional neural networks (CNNs) and Transformers suffer from strong dependence on large-scale annotated data and limited generalization capability when dealing with hand pose reconstruction in complex scenarios. To address these issues, a diffusion-based end-to-end hand pose reconstruction network (DEHPR) is proposed. This method employs a diffusion model to directly generate and refine 3D predictions, thereby reducing spatial uncertainties inherent in 2D-to-3D modeling paradigms. By incorporating an end-to-end framework that reprojects multiple 3D candidate predictions to select optimal joint positions, the approach ultimately produces accurate hand pose estimations. Comprehensive evaluations conducted on HO3D V2, DexYCB, …

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Cross-Domain Crowd Counting Model Based On Frequency Domain Enhancement, De Zhang, Zishan Liang, Ningning Liu Jan 2026

Cross-Domain Crowd Counting Model Based On Frequency Domain Enhancement, De Zhang, Zishan Liang, Ningning Liu

Journal of System Simulation

Abstract: Crowd counting takes video surveillance data as input and can be applied to the construction of city digital twin platforms, virtual city modeling and smart city management, etc. However, when there are data domain differences between the application scenario and training scenario, counting performance often significantly decreases. A cross-domain crowd counting model based on frequency domain enhancement is proposed. To alleviate the distribution differences between domains, a frequency domain feature enhancement module and a domain invariant frequency domain adapter module are constructed: the former uses discrete cosine transform to extract key statistical features to enhance spatial representation ability, while …

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Research On Real-Time Animatable Human Avatar Generation Via 3d Gaussian Splatting, Yuyou Zhong, Xukun Shen, Yong Hu Jan 2026

Research On Real-Time Animatable Human Avatar Generation Via 3d Gaussian Splatting, Yuyou Zhong, Xukun Shen, Yong Hu

Journal of System Simulation

Abstract: Real-time animatable 3D human avatar generation technology hold significant application value in fields such as virtual reality and remote collaboration. To address the limitations of existing methods in detail modeling, real-time performance, and robustness under novel pose driving, an efficient human avatar generation and driving method based on 3D Gaussian splatting (3DGS) is proposed. This method integrates optimized parametric human reconstruction, tri-plane feature encoding, and dynamic offset prediction to achieve efficient modeling from monocular video input. By introducing a skeleton binding and visibility analysis strategy, while designing a multi-scale regularization loss to address the overfitting problem. Simulation experiments demonstrate …

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